To　address　the　problem　of　incomplete　segmentation　of　large　objects　and　miss-segmentation　of　tiny　objects　that　is　universally　existing　in　semantic　segmentation　algorithms,　PACAMNet,　a　real-time　segmentation　network　based　on　short-term　dense　concatenate　of　parallel　atrous　convolution　and　fusion　of　attentional　features　is　proposed,　called　PACAMNet.　First,　parallel　atrous　convolution　is　introduced　to　improve　the　short-term　dense　concatenate　module.　By　adjusting　the　size　of　the　atrous　factor,　multi-scale　semantic　information　is　obtained　to　ensure　that　the　last　layer　of　the　module　can　also　obtain　rich　input　feature　maps.　Second,　attention　feature　fusion　module　is　proposed　to　align　the　receptive　fields　of　deep　and　shallow　feature　maps　via　depth-separable　convolutions　with　different　sizes,　and　the　channel　attention　mechanism　is　used　to　generate　weights　to　effectively　fuse　the　deep　and　shallow　feature　maps.　Finally,　experiments　are　carried　out　based　on　both　Cityscapes　and　CamVid　datasets,　and　the　segmentation　accuracy　achieve　77.4%　and　74.0%　at　the　inference　speeds　of　98.7　FPS　and　134.6　FPS,　respectively.　Compared　with　other　methods,　PACAMNet　improves　the　inference　speed　of　the　model　while　ensuring　higher　segmentation　accuracy,　so　PACAMNet　achieve　a　better　balance　between　segmentation　accuracy　and　inference　speed.